Hierarchical Class Level Attribute Guided Generative Meta Learning for Pest Image Zero-shot Learning

Shansong Wang, Qingtian Zeng, Weijian Ni, Xue Zhang, Cheng Cheng

Abstract: Existing pest image classification models require a large number of labeled training images. However, labels for most pest images in the real world do not exist. Therefore, the zero-shot learning method based on generative meta-learning provides an effective solution, which first uses attributes to transfer knowledge from seen classes to unseen classes, and then synthesizes the features of unseen classes. We observe that seen and unseen classes share the same high-level attributes, which can be used to learn a shared set of optimal parameters for seen and unseen classes. Therefore, we propose a novel Hierarchical Class level Attribute guided Generative meta model for pest image Zero-shot Learning (HCAG-ZSL). HCAG-ZSL uses the pre-built Taxonomic Attribute Tree to get the high-level attributes corresponding to the class attributes. These attributes are then fed into a well-designed generator to generate visual features. Extensive experiments show that the proposed model outperforms state-of-the-art generative meta models.